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Discrimination of moldy wheat using terahertz imaging combined with multivariate classification

机译：使用太赫兹成像的发霉小麦与多变量分类相结合的歧视

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摘要

Terahertz (THz) imaging was employed to develop a novel method for discriminating wheat of varying states of moldiness. Spectral data, in the range of 0.2-1.6 THz, were extracted from regions of interest (ROIs) in the THz images. Principal component analysis (PCA) was used to evaluate the spectral data and determine the cluster trend. Six optimal frequencies were selected by implementing PCA directly for each image's ROI. Classification models for moldy wheat identification were established using the support vector machine (SVM) method, a partial least-squares regression analysis, and the back propagation neural network method. The models developed from these methods were based on the full and optimal frequencies, using the top three principal components as input variables. The PCA-SVM method achieved a prediction accuracy of over 95%, and was implemented at every pixel in the images to visually demonstrate the moldy wheat classification method. Our results indicate that THz imaging combined with chemometric algorithms is efficient and practical for the discrimination of moldy wheat.
机译：使用太赫兹（THz）成像，用于开发一种辨别多元态霉菌状态的小麦的新方法。光谱数据，在0.2-1.6至16至16至16至16至6至6至6至6至6至6至6至6至6至6至6至6至6六个范围内。主要成分分析（PCA）用于评估光谱数据并确定群集趋势。通过直接为每个图像的ROI实现PCA来选择六个最佳频率。使用支持向量机（SVM）方法，局部最小二乘回归分析和后传播神经网络方法建立了发霉小麦识别的分类模型。从这些方法开发的模型基于完整和最佳频率，使用前三个主组件作为输入变量。 PCA-SVM方法实现了超过95％的预测精度，并且在图像中的每个像素处实现，以在视觉上证明发霉的小麦分类方法。我们的结果表明，与化学计量算法相结合的THz成像对于歧视发霉小麦的鉴别是有效且实用的。

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  • 来源
    《RSC Advances》 |2015年第114期|共8页
  • 作者

    Yuying Jiang; Hongyi Ge; Feiyu Lian;

  • 作者单位

    State Key Laboratory of Transducer Technology Institute of Electronics Chinese Academy of Sciences Beijing 100080 China;

    Key Laboratory of Grain Information Processing &

    Control Ministry of Education Henan University of Technology Zhengzhou 450001 China.;

    Key Laboratory of Grain Information Processing &

    Control Ministry of Education Henan University of Technology Zhengzhou 450001 China.;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 化学;
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